Overview

Dataset statistics

Number of variables24
Number of observations1460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory273.9 KiB
Average record size in memory192.1 B

Variable types

Numeric19
Categorical5

Alerts

1stFlrSF is highly overall correlated with SalePrice and 1 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with GrLivArea and 1 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
Fireplaces is highly overall correlated with SalePriceHigh correlation
FullBath is highly overall correlated with GarageYrBlt and 4 other fieldsHigh correlation
GarageArea is highly overall correlated with GarageCars and 3 other fieldsHigh correlation
GarageCars is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageYrBlt is highly overall correlated with FullBath and 4 other fieldsHigh correlation
GrLivArea is highly overall correlated with BedroomAbvGr and 6 other fieldsHigh correlation
HouseCategory is highly overall correlated with GrLivArea and 1 other fieldsHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
OverallQual is highly overall correlated with FullBath and 5 other fieldsHigh correlation
SalePrice is highly overall correlated with 1stFlrSF and 9 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with BedroomAbvGr and 4 other fieldsHigh correlation
TotalBsmtSF is highly overall correlated with 1stFlrSF and 1 other fieldsHigh correlation
KitchenAbvGr is highly imbalanced (85.7%)Imbalance
MSSubClass has 536 (36.7%) zerosZeros
LotFrontage has 24 (1.6%) zerosZeros
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
MoSold has 58 (4.0%) zerosZeros
HouseCategory has 36 (2.5%) zerosZeros

Reproduction

Analysis started2024-04-27 21:14:51.138134
Analysis finished2024-04-27 21:15:13.931974
Duration22.79 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

MSSubClass
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21704271
Minimum0
Maximum1
Zeros536
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:13.988678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.17647059
Q30.29411765
95-th percentile0.82352941
Maximum1
Range1
Interquartile range (IQR)0.29411765

Descriptive statistics

Standard deviation0.24882689
Coefficient of variation (CV)1.1464421
Kurtosis1.580188
Mean0.21704271
Median Absolute Deviation (MAD)0.17647059
Skewness1.4076567
Sum316.88235
Variance0.06191482
MonotonicityNot monotonic
2024-04-27T17:15:14.061360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 536
36.7%
0.2352941176 299
20.5%
0.1764705882 144
 
9.9%
0.5882352941 87
 
6.0%
0.05882352941 69
 
4.7%
0.8235294118 63
 
4.3%
0.2941176471 60
 
4.1%
0.3529411765 58
 
4.0%
0.4117647059 52
 
3.6%
1 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
0 536
36.7%
0.05882352941 69
 
4.7%
0.1176470588 4
 
0.3%
0.1470588235 12
 
0.8%
0.1764705882 144
 
9.9%
0.2352941176 299
20.5%
0.2941176471 60
 
4.1%
0.3235294118 16
 
1.1%
0.3529411765 58
 
4.0%
0.3823529412 20
 
1.4%
ValueCountFrequency (%)
1 30
 
2.1%
0.9411764706 10
 
0.7%
0.8235294118 63
 
4.3%
0.5882352941 87
 
6.0%
0.4117647059 52
 
3.6%
0.3823529412 20
 
1.4%
0.3529411765 58
 
4.0%
0.3235294118 16
 
1.1%
0.2941176471 60
 
4.1%
0.2352941176 299
20.5%

LotFrontage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct232
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17121552
Minimum0
Maximum1
Zeros24
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:14.135011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.047945205
Q10.13356164
median0.16780822
Q30.20565068
95-th percentile0.29469178
Maximum1
Range1
Interquartile range (IQR)0.072089041

Descriptive statistics

Standard deviation0.081551993
Coefficient of variation (CV)0.47631192
Kurtosis15.473232
Mean0.17121552
Median Absolute Deviation (MAD)0.034246575
Skewness1.9164596
Sum249.97466
Variance0.0066507276
MonotonicityNot monotonic
2024-04-27T17:15:14.212845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1335616438 143
 
9.8%
0.1678082192 70
 
4.8%
0.2020547945 69
 
4.7%
0.09931506849 57
 
3.9%
0.1849315068 54
 
3.7%
0.1506849315 46
 
3.2%
0.2191780822 41
 
2.8%
0.1952054795 25
 
1.7%
0 24
 
1.6%
0.2363013699 23
 
1.6%
Other values (222) 908
62.2%
ValueCountFrequency (%)
0 24
1.6%
0.0102739726 19
1.3%
0.01849315068 3
 
0.2%
0.02465753425 1
 
0.1%
0.0301369863 1
 
0.1%
0.03082191781 6
 
0.4%
0.03767123288 5
 
0.3%
0.04109589041 1
 
0.1%
0.04452054795 10
0.7%
0.04794520548 9
 
0.6%
ValueCountFrequency (%)
1 2
0.1%
0.551369863 1
0.1%
0.5239726027 2
0.1%
0.5034246575 1
0.1%
0.4760273973 1
0.1%
0.4520547945 1
0.1%
0.451369863 2
0.1%
0.448630137 1
0.1%
0.4417808219 1
0.1%
0.4383561644 1
0.1%

LotArea
Real number (ℝ)

HIGH CORRELATION 

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043080362
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:14.290849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0094028839
Q10.029229475
median0.038227114
Q30.048150226
95-th percentile0.075258361
Maximum1
Range1
Interquartile range (IQR)0.018920751

Descriptive statistics

Standard deviation0.046653415
Coefficient of variation (CV)1.0829393
Kurtosis203.24327
Mean0.043080362
Median Absolute Deviation (MAD)0.0093388488
Skewness12.207688
Sum62.897329
Variance0.0021765412
MonotonicityNot monotonic
2024-04-27T17:15:14.369581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02757718105 25
 
1.7%
0.03879501741 24
 
1.6%
0.02196826287 17
 
1.2%
0.03599055832 14
 
1.0%
0.03318609923 14
 
1.0%
0.04440393559 14
 
1.0%
0.001776157424 10
 
0.7%
0.0289794106 9
 
0.6%
0.03645796817 8
 
0.5%
0.03190072215 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
0 1
 
0.1%
0.0008273154315 1
 
0.1%
0.0008927528103 1
 
0.1%
0.001056346257 1
 
0.1%
0.001089064947 2
 
0.1%
0.001383533151 1
 
0.1%
0.001776157424 10
0.7%
0.002659562037 1
 
0.1%
0.002757718105 2
 
0.1%
0.00289794106 1
 
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.7635607282 1
0.1%
0.7371053308 1
0.1%
0.5321414382 1
0.1%
0.3246675547 1
0.1%
0.2925378018 1
0.1%
0.2612821052 1
0.1%
0.2440066372 1
0.1%
0.2427119119 1
0.1%
0.2421510201 1
0.1%

OverallQual
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56659056
Minimum0
Maximum1
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:14.437101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33333333
Q10.44444444
median0.55555556
Q30.66666667
95-th percentile0.77777778
Maximum1
Range1
Interquartile range (IQR)0.22222222

Descriptive statistics

Standard deviation0.15366628
Coefficient of variation (CV)0.27121222
Kurtosis0.096292778
Mean0.56659056
Median Absolute Deviation (MAD)0.11111111
Skewness0.21694393
Sum827.22222
Variance0.023613327
MonotonicityNot monotonic
2024-04-27T17:15:14.497058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.4444444444 397
27.2%
0.5555555556 374
25.6%
0.6666666667 319
21.8%
0.7777777778 168
11.5%
0.3333333333 116
 
7.9%
0.8888888889 43
 
2.9%
0.2222222222 20
 
1.4%
1 18
 
1.2%
0.1111111111 3
 
0.2%
0 2
 
0.1%
ValueCountFrequency (%)
0 2
 
0.1%
0.1111111111 3
 
0.2%
0.2222222222 20
 
1.4%
0.3333333333 116
 
7.9%
0.4444444444 397
27.2%
0.5555555556 374
25.6%
0.6666666667 319
21.8%
0.7777777778 168
11.5%
0.8888888889 43
 
2.9%
1 18
 
1.2%
ValueCountFrequency (%)
1 18
 
1.2%
0.8888888889 43
 
2.9%
0.7777777778 168
11.5%
0.6666666667 319
21.8%
0.5555555556 374
25.6%
0.4444444444 397
27.2%
0.3333333333 116
 
7.9%
0.2222222222 20
 
1.4%
0.1111111111 3
 
0.2%
0 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57191781
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:14.557962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.375
Q10.5
median0.5
Q30.625
95-th percentile0.875
Maximum1
Range1
Interquartile range (IQR)0.125

Descriptive statistics

Standard deviation0.13909992
Coefficient of variation (CV)0.24321662
Kurtosis1.1064135
Mean0.57191781
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum835
Variance0.019348787
MonotonicityNot monotonic
2024-04-27T17:15:14.617734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.5 821
56.2%
0.625 252
 
17.3%
0.75 205
 
14.0%
0.875 72
 
4.9%
0.375 57
 
3.9%
0.25 25
 
1.7%
1 22
 
1.5%
0.125 5
 
0.3%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
0.125 5
 
0.3%
0.25 25
 
1.7%
0.375 57
 
3.9%
0.5 821
56.2%
0.625 252
 
17.3%
0.75 205
 
14.0%
0.875 72
 
4.9%
1 22
 
1.5%
ValueCountFrequency (%)
1 22
 
1.5%
0.875 72
 
4.9%
0.75 205
 
14.0%
0.625 252
 
17.3%
0.5 821
56.2%
0.375 57
 
3.9%
0.25 25
 
1.7%
0.125 5
 
0.3%
0 1
 
0.1%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.078603779
Minimum0
Maximum1
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:14.689634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.067948264
Q30.12619596
95-th percentile0.22572644
Maximum1
Range1
Interquartile range (IQR)0.12619596

Descriptive statistics

Standard deviation0.080811143
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean0.078603779
Median Absolute Deviation (MAD)0.067948264
Skewness1.6855031
Sum114.76152
Variance0.0065304408
MonotonicityNot monotonic
2024-04-27T17:15:14.774583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
0.004252303331 12
 
0.8%
0.002834868887 9
 
0.6%
0.1215450035 5
 
0.3%
0.1172927002 5
 
0.3%
0.003543586109 5
 
0.3%
0.1658398299 5
 
0.3%
0.1091424522 5
 
0.3%
0.09922041106 4
 
0.3%
0.09798015592 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
0.0003543586109 1
 
0.1%
0.002834868887 9
 
0.6%
0.003543586109 5
 
0.3%
0.004252303331 12
 
0.8%
0.004429482636 1
 
0.1%
0.004783841247 1
 
0.1%
0.004961020553 3
 
0.2%
0.00584691708 1
 
0.1%
0.006201275691 1
 
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.4004252303 1
0.1%
0.3876683203 1
0.1%
0.3713678242 1
0.1%
0.3373493976 1
0.1%
0.3330970943 1
0.1%
0.3206945429 1
0.1%
0.3130758327 1
0.1%
0.3049255847 1
0.1%
0.3004961021 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24282552
Minimum0
Maximum1
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:14.871940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.095462329
median0.20440925
Q30.34589041
95-th percentile0.62842466
Maximum1
Range1
Interquartile range (IQR)0.25042808

Descriptive statistics

Standard deviation0.18915537
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean0.24282552
Median Absolute Deviation (MAD)0.12328767
Skewness0.92026845
Sum354.52526
Variance0.035779756
MonotonicityNot monotonic
2024-04-27T17:15:14.966469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
0.3116438356 9
 
0.6%
0.1643835616 8
 
0.5%
0.2568493151 7
 
0.5%
0.1284246575 7
 
0.5%
0.2448630137 7
 
0.5%
0.1155821918 6
 
0.4%
0.2675513699 6
 
0.4%
0.2876712329 6
 
0.4%
0.1883561644 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
0.005993150685 1
 
0.1%
0.006421232877 1
 
0.1%
0.009845890411 2
 
0.1%
0.01113013699 1
 
0.1%
0.01241438356 1
 
0.1%
0.01284246575 1
 
0.1%
0.01369863014 2
 
0.1%
0.01498287671 1
 
0.1%
0.0154109589 4
 
0.3%
ValueCountFrequency (%)
1 1
0.1%
0.9216609589 1
0.1%
0.9079623288 1
0.1%
0.8758561644 1
0.1%
0.8741438356 1
0.1%
0.8570205479 1
0.1%
0.8428938356 1
0.1%
0.8283390411 1
0.1%
0.8244863014 1
0.1%
0.8163527397 1
0.1%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17306538
Minimum0
Maximum1
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:15.061013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.084991817
Q10.13023732
median0.16227496
Q30.21247954
95-th percentile0.28690671
Maximum1
Range1
Interquartile range (IQR)0.082242226

Descriptive statistics

Standard deviation0.071801199
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean0.17306538
Median Absolute Deviation (MAD)0.038379705
Skewness1.5242545
Sum252.67545
Variance0.0051554121
MonotonicityNot monotonic
2024-04-27T17:15:15.157818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
0.1414075286 35
 
2.4%
0.1099836334 17
 
1.2%
0.1492635025 15
 
1.0%
0.170212766 14
 
1.0%
0.1335515548 13
 
0.9%
0.125695581 12
 
0.8%
0.1191489362 12
 
0.8%
0.1463175123 11
 
0.8%
0.1276595745 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
0.01718494272 1
 
0.1%
0.03109656301 1
 
0.1%
0.04320785597 3
 
0.2%
0.0441898527 1
 
0.1%
0.04746317512 1
 
0.1%
0.05220949264 1
 
0.1%
0.0589198036 1
 
0.1%
0.06088379705 1
 
0.1%
0.06284779051 7
 
0.5%
ValueCountFrequency (%)
1 1
0.1%
0.5247135843 1
0.1%
0.5237315876 1
0.1%
0.5135842881 1
0.1%
0.5063829787 1
0.1%
0.4309328969 1
0.1%
0.4130932897 1
0.1%
0.4 1
0.1%
0.3921440262 1
0.1%
0.3914893617 1
0.1%

1stFlrSF
Real number (ℝ)

HIGH CORRELATION 

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19013922
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:15.248589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.077776503
Q10.12574575
median0.17278568
Q30.24259982
95-th percentile0.34356356
Maximum1
Range1
Interquartile range (IQR)0.11685406

Descriptive statistics

Standard deviation0.088707604
Coefficient of variation (CV)0.46654028
Kurtosis5.7458415
Mean0.19013922
Median Absolute Deviation (MAD)0.053809087
Skewness1.3767566
Sum277.60326
Variance0.007869039
MonotonicityNot monotonic
2024-04-27T17:15:15.352152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1216154199 25
 
1.7%
0.1620009179 16
 
1.1%
0.1326296466 14
 
1.0%
0.1284993116 12
 
0.8%
0.117944011 12
 
0.8%
0.07755851308 11
 
0.8%
0.06792106471 9
 
0.6%
0.1106011932 9
 
0.6%
0.03418999541 7
 
0.5%
0.1436438733 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
0 1
 
0.1%
0.008719596145 1
 
0.1%
0.02386415787 1
 
0.1%
0.03350160624 1
 
0.1%
0.03418999541 7
0.5%
0.03694355209 1
 
0.1%
0.0426801285 5
0.3%
0.04382744378 1
 
0.1%
0.04405690684 1
 
0.1%
0.0463515374 1
 
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.6640660854 1
0.1%
0.6434144103 1
0.1%
0.5883432767 1
0.1%
0.5275355668 1
0.1%
0.5025240936 1
0.1%
0.5004589261 1
0.1%
0.4841670491 1
0.1%
0.4765947682 1
0.1%
0.4745296007 1
0.1%

GrLivArea
Real number (ℝ)

HIGH CORRELATION 

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22258171
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:15.445717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.096834966
Q10.14986812
median0.21288621
Q30.27180671
95-th percentile0.40167671
Maximum1
Range1
Interquartile range (IQR)0.12193858

Descriptive statistics

Standard deviation0.098997811
Coefficient of variation (CV)0.44477066
Kurtosis4.8951206
Mean0.22258171
Median Absolute Deviation (MAD)0.061416729
Skewness1.3665604
Sum324.96929
Variance0.0098005667
MonotonicityNot monotonic
2024-04-27T17:15:15.537247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0998492841 22
 
1.5%
0.1330067822 14
 
1.0%
0.1055011304 11
 
0.8%
0.2113790505 10
 
0.7%
0.09683496609 10
 
0.7%
0.1631499623 9
 
0.6%
0.1088922381 9
 
0.6%
0.09080633007 8
 
0.5%
0.1428033157 8
 
0.5%
0.2626224567 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
0 1
 
0.1%
0.01959306707 1
 
0.1%
0.02750565185 1
 
0.1%
0.03504144687 1
 
0.1%
0.0510550113 1
 
0.1%
0.05312735494 1
 
0.1%
0.0557648832 6
0.4%
0.06367746797 2
 
0.1%
0.06725697061 1
 
0.1%
0.06763376036 1
 
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.8180105501 1
0.1%
0.780331575 1
0.1%
0.7501883949 1
0.1%
0.6203843255 1
0.1%
0.6168048229 1
0.1%
0.5951394122 1
0.1%
0.5864732479 1
0.1%
0.5766767144 1
0.1%
0.5548229088 1
0.1%

FullBath
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.6666666666666666
768 
0.3333333333333333
650 
1.0
 
33
0.0
 
9

Length

Max length18
Median length18
Mean length17.568493
Min length3

Characters and Unicode

Total characters25650
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6666666666666666
2nd row0.6666666666666666
3rd row0.6666666666666666
4th row0.3333333333333333
5th row0.6666666666666666

Common Values

ValueCountFrequency (%)
0.6666666666666666 768
52.6%
0.3333333333333333 650
44.5%
1.0 33
 
2.3%
0.0 9
 
0.6%

Length

2024-04-27T17:15:15.622024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T17:15:15.698015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.6666666666666666 768
52.6%
0.3333333333333333 650
44.5%
1.0 33
 
2.3%
0.0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
6 12288
47.9%
3 10400
40.5%
0 1469
 
5.7%
. 1460
 
5.7%
1 33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24190
94.3%
Other Punctuation 1460
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 12288
50.8%
3 10400
43.0%
0 1469
 
6.1%
1 33
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 12288
47.9%
3 10400
40.5%
0 1469
 
5.7%
. 1460
 
5.7%
1 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 12288
47.9%
3 10400
40.5%
0 1469
 
5.7%
. 1460
 
5.7%
1 33
 
0.1%

BedroomAbvGr
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35830479
Minimum0
Maximum1
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:15.763751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.375
Q30.375
95-th percentile0.5
Maximum1
Range1
Interquartile range (IQR)0.125

Descriptive statistics

Standard deviation0.10197226
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean0.35830479
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum523.125
Variance0.010398341
MonotonicityNot monotonic
2024-04-27T17:15:15.827494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.375 804
55.1%
0.25 358
24.5%
0.5 213
 
14.6%
0.125 50
 
3.4%
0.625 21
 
1.4%
0.75 7
 
0.5%
0 6
 
0.4%
1 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
0.125 50
 
3.4%
0.25 358
24.5%
0.375 804
55.1%
0.5 213
 
14.6%
0.625 21
 
1.4%
0.75 7
 
0.5%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
0.75 7
 
0.5%
0.625 21
 
1.4%
0.5 213
 
14.6%
0.375 804
55.1%
0.25 358
24.5%
0.125 50
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.3333333333333333
1392 
0.6666666666666666
 
65
1.0
 
2
0.0
 
1

Length

Max length18
Median length18
Mean length17.969178
Min length3

Characters and Unicode

Total characters26235
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.3333333333333333
2nd row0.3333333333333333
3rd row0.3333333333333333
4th row0.3333333333333333
5th row0.3333333333333333

Common Values

ValueCountFrequency (%)
0.3333333333333333 1392
95.3%
0.6666666666666666 65
 
4.5%
1.0 2
 
0.1%
0.0 1
 
0.1%

Length

2024-04-27T17:15:15.903494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T17:15:15.972127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.3333333333333333 1392
95.3%
0.6666666666666666 65
 
4.5%
1.0 2
 
0.1%
0.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 22272
84.9%
0 1461
 
5.6%
. 1460
 
5.6%
6 1040
 
4.0%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24775
94.4%
Other Punctuation 1460
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 22272
89.9%
0 1461
 
5.9%
6 1040
 
4.2%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26235
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 22272
84.9%
0 1461
 
5.6%
. 1460
 
5.6%
6 1040
 
4.0%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 22272
84.9%
0 1461
 
5.6%
. 1460
 
5.6%
6 1040
 
4.0%
1 2
 
< 0.1%

TotRmsAbvGrd
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37648402
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:16.036900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16666667
Q10.25
median0.33333333
Q30.41666667
95-th percentile0.66666667
Maximum1
Range1
Interquartile range (IQR)0.16666667

Descriptive statistics

Standard deviation0.13544944
Coefficient of variation (CV)0.35977474
Kurtosis0.88076157
Mean0.37648402
Median Absolute Deviation (MAD)0.083333333
Skewness0.67634084
Sum549.66667
Variance0.018346551
MonotonicityNot monotonic
2024-04-27T17:15:16.125456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.3333333333 402
27.5%
0.4166666667 329
22.5%
0.25 275
18.8%
0.5 187
12.8%
0.1666666667 97
 
6.6%
0.5833333333 75
 
5.1%
0.6666666667 47
 
3.2%
0.75 18
 
1.2%
0.08333333333 17
 
1.2%
0.8333333333 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
0.08333333333 17
 
1.2%
0.1666666667 97
 
6.6%
0.25 275
18.8%
0.3333333333 402
27.5%
0.4166666667 329
22.5%
0.5 187
12.8%
0.5833333333 75
 
5.1%
0.6666666667 47
 
3.2%
0.75 18
 
1.2%
ValueCountFrequency (%)
1 1
 
0.1%
0.8333333333 11
 
0.8%
0.75 18
 
1.2%
0.6666666667 47
 
3.2%
0.5833333333 75
 
5.1%
0.5 187
12.8%
0.4166666667 329
22.5%
0.3333333333 402
27.5%
0.25 275
18.8%
0.1666666667 97
 
6.6%

Fireplaces
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
690 
0.3333333333333333
650 
0.6666666666666666
115 
1.0
 
5

Length

Max length18
Median length18
Mean length10.859589
Min length3

Characters and Unicode

Total characters15855
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.3333333333333333
3rd row0.3333333333333333
4th row0.3333333333333333
5th row0.3333333333333333

Common Values

ValueCountFrequency (%)
0.0 690
47.3%
0.3333333333333333 650
44.5%
0.6666666666666666 115
 
7.9%
1.0 5
 
0.3%

Length

2024-04-27T17:15:16.204493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T17:15:16.270862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 690
47.3%
0.3333333333333333 650
44.5%
0.6666666666666666 115
 
7.9%
1.0 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
3 10400
65.6%
0 2150
 
13.6%
6 1840
 
11.6%
. 1460
 
9.2%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14395
90.8%
Other Punctuation 1460
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 10400
72.2%
0 2150
 
14.9%
6 1840
 
12.8%
1 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15855
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 10400
65.6%
0 2150
 
13.6%
6 1840
 
11.6%
. 1460
 
9.2%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 10400
65.6%
0 2150
 
13.6%
6 1840
 
11.6%
. 1460
 
9.2%
1 5
 
< 0.1%

GarageYrBlt
Real number (ℝ)

HIGH CORRELATION 

Distinct152
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70517808
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:16.350593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27272727
Q10.54545455
median0.70909091
Q30.91818182
95-th percentile0.97272727
Maximum1
Range1
Interquartile range (IQR)0.37272727

Descriptive statistics

Standard deviation0.22322227
Coefficient of variation (CV)0.31654738
Kurtosis-0.52405786
Mean0.70517808
Median Absolute Deviation (MAD)0.19090909
Skewness-0.56393383
Sum1029.56
Variance0.049828184
MonotonicityNot monotonic
2024-04-27T17:15:16.434286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9545454545 65
 
4.5%
0.9636363636 59
 
4.0%
0.9454545455 53
 
3.6%
0.9363636364 50
 
3.4%
0.9727272727 49
 
3.4%
0.7 35
 
2.4%
0.8909090909 31
 
2.1%
0.9 30
 
2.1%
0.6909090909 29
 
2.0%
0.9818181818 29
 
2.0%
Other values (142) 1030
70.5%
ValueCountFrequency (%)
0 1
 
0.1%
0.05454545455 1
 
0.1%
0.07272727273 1
 
0.1%
0.09090909091 3
 
0.2%
0.1272727273 2
 
0.1%
0.1363636364 2
 
0.1%
0.1454545455 5
 
0.3%
0.1636363636 2
 
0.1%
0.1818181818 14
1.0%
0.1909090909 3
 
0.2%
ValueCountFrequency (%)
1 3
 
0.2%
0.9909090909 21
 
1.4%
0.9818181818 29
2.0%
0.9727272727 49
3.4%
0.9636363636 59
4.0%
0.9545454545 65
4.5%
0.9454545455 53
3.6%
0.9363636364 50
3.4%
0.9272727273 26
 
1.8%
0.92 1
 
0.1%

GarageCars
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.5
824 
0.25
369 
0.75
181 
0.0
 
81
1.0
 
5

Length

Max length4
Median length3
Mean length3.3767123
Min length3

Characters and Unicode

Total characters4930
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.75
5th row0.75

Common Values

ValueCountFrequency (%)
0.5 824
56.4%
0.25 369
25.3%
0.75 181
 
12.4%
0.0 81
 
5.5%
1.0 5
 
0.3%

Length

2024-04-27T17:15:16.511403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T17:15:16.580237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.5 824
56.4%
0.25 369
25.3%
0.75 181
 
12.4%
0.0 81
 
5.5%
1.0 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1541
31.3%
. 1460
29.6%
5 1374
27.9%
2 369
 
7.5%
7 181
 
3.7%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3470
70.4%
Other Punctuation 1460
29.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1541
44.4%
5 1374
39.6%
2 369
 
10.6%
7 181
 
5.2%
1 5
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1541
31.3%
. 1460
29.6%
5 1374
27.9%
2 369
 
7.5%
7 181
 
3.7%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1541
31.3%
. 1460
29.6%
5 1374
27.9%
2 369
 
7.5%
7 181
 
3.7%
1 5
 
0.1%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3335544
Minimum0
Maximum1
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:16.660594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.23589563
median0.33850494
Q30.40620592
95-th percentile0.59950635
Maximum1
Range1
Interquartile range (IQR)0.1703103

Descriptive statistics

Standard deviation0.15077915
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean0.3335544
Median Absolute Deviation (MAD)0.084626234
Skewness0.17998091
Sum486.98942
Variance0.022734354
MonotonicityNot monotonic
2024-04-27T17:15:16.746272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
0.3102961918 49
 
3.4%
0.4062059238 47
 
3.2%
0.1692524683 38
 
2.6%
0.341325811 34
 
2.3%
0.3723554302 33
 
2.3%
0.2031029619 27
 
1.8%
0.2820874471 25
 
1.7%
0.1861777151 24
 
1.6%
0.3385049365 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
0.1128349788 2
 
0.1%
0.1156558533 1
 
0.1%
0.1269393512 9
 
0.6%
0.1311706629 1
 
0.1%
0.1332863188 1
 
0.1%
0.1354019746 1
 
0.1%
0.1396332863 1
 
0.1%
0.1410437236 4
 
0.3%
0.1445698166 3
 
0.2%
ValueCountFrequency (%)
1 1
0.1%
0.9802538787 1
0.1%
0.9562764457 1
0.1%
0.880112835 1
0.1%
0.8603667137 1
0.1%
0.8222849083 1
0.1%
0.7997179126 1
0.1%
0.7538787024 1
0.1%
0.7425952045 1
0.1%
0.7418899859 2
0.1%

OpenPorchSF
Real number (ℝ)

ZEROS 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.085302146
Minimum0
Maximum1
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:16.834121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.045703839
Q30.12431444
95-th percentile0.32001828
Maximum1
Range1
Interquartile range (IQR)0.12431444

Descriptive statistics

Standard deviation0.12112619
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean0.085302146
Median Absolute Deviation (MAD)0.045703839
Skewness2.3643417
Sum124.54113
Variance0.014671555
MonotonicityNot monotonic
2024-04-27T17:15:16.921889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
0.06581352834 29
 
2.0%
0.08775137112 22
 
1.5%
0.0365630713 21
 
1.4%
0.0731261426 19
 
1.3%
0.08226691042 19
 
1.3%
0.04387568556 16
 
1.1%
0.05484460695 16
 
1.1%
0.1096892139 15
 
1.0%
0.07129798903 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
0.00731261426 1
 
0.1%
0.01462522852 1
 
0.1%
0.01828153565 1
 
0.1%
0.02010968921 1
 
0.1%
0.02193784278 3
 
0.2%
0.02742230347 1
 
0.1%
0.02925045704 8
 
0.5%
0.0310786106 2
 
0.1%
0.03290676417 5
 
0.3%
ValueCountFrequency (%)
1 1
0.1%
0.9561243144 1
0.1%
0.9177330896 1
0.1%
0.7641681901 1
0.1%
0.7422303473 1
0.1%
0.6654478976 1
0.1%
0.6234003656 1
0.1%
0.5831809872 1
0.1%
0.5703839122 2
0.1%
0.5557586837 1
0.1%

MoSold
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48381071
Minimum0
Maximum1
Zeros58
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:16.994188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.090909091
Q10.36363636
median0.45454545
Q30.63636364
95-th percentile0.90909091
Maximum1
Range1
Interquartile range (IQR)0.27272727

Descriptive statistics

Standard deviation0.2457842
Coefficient of variation (CV)0.50801728
Kurtosis-0.40410934
Mean0.48381071
Median Absolute Deviation (MAD)0.18181818
Skewness0.21205299
Sum706.36364
Variance0.060409873
MonotonicityNot monotonic
2024-04-27T17:15:17.058070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.4545454545 253
17.3%
0.5454545455 234
16.0%
0.3636363636 204
14.0%
0.2727272727 141
9.7%
0.6363636364 122
8.4%
0.1818181818 106
7.3%
0.8181818182 89
 
6.1%
0.9090909091 79
 
5.4%
0.7272727273 63
 
4.3%
1 59
 
4.0%
Other values (2) 110
7.5%
ValueCountFrequency (%)
0 58
 
4.0%
0.09090909091 52
 
3.6%
0.1818181818 106
7.3%
0.2727272727 141
9.7%
0.3636363636 204
14.0%
0.4545454545 253
17.3%
0.5454545455 234
16.0%
0.6363636364 122
8.4%
0.7272727273 63
 
4.3%
0.8181818182 89
 
6.1%
ValueCountFrequency (%)
1 59
 
4.0%
0.9090909091 79
 
5.4%
0.8181818182 89
 
6.1%
0.7272727273 63
 
4.3%
0.6363636364 122
8.4%
0.5454545455 234
16.0%
0.4545454545 253
17.3%
0.3636363636 204
14.0%
0.2727272727 141
9.7%
0.1818181818 106
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.75
338 
0.25
329 
0.0
314 
0.5
304 
1.0
175 

Length

Max length4
Median length3
Mean length3.4568493
Min length3

Characters and Unicode

Total characters5047
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.25
3rd row0.5
4th row0.0
5th row0.5

Common Values

ValueCountFrequency (%)
0.75 338
23.2%
0.25 329
22.5%
0.0 314
21.5%
0.5 304
20.8%
1.0 175
12.0%

Length

2024-04-27T17:15:17.124847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T17:15:17.193849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.75 338
23.2%
0.25 329
22.5%
0.0 314
21.5%
0.5 304
20.8%
1.0 175
12.0%

Most occurring characters

ValueCountFrequency (%)
0 1774
35.1%
. 1460
28.9%
5 971
19.2%
7 338
 
6.7%
2 329
 
6.5%
1 175
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3587
71.1%
Other Punctuation 1460
28.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1774
49.5%
5 971
27.1%
7 338
 
9.4%
2 329
 
9.2%
1 175
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 1460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5047
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1774
35.1%
. 1460
28.9%
5 971
19.2%
7 338
 
6.7%
2 329
 
6.5%
1 175
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1774
35.1%
. 1460
28.9%
5 971
19.2%
7 338
 
6.7%
2 329
 
6.5%
1 175
 
3.5%

ada_embedding
Real number (ℝ)

Distinct1402
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01122737
Minimum-0.04465026
Maximum0.021723356
Zeros0
Zeros (%)0.0%
Negative1289
Negative (%)88.3%
Memory size11.5 KiB
2024-04-27T17:15:17.276125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.04465026
5-th percentile-0.028981373
Q1-0.017517184
median-0.010191985
Q3-0.0047376036
95-th percentile0.0034181204
Maximum0.021723356
Range0.066373616
Interquartile range (IQR)0.012779581

Descriptive statistics

Standard deviation0.0097662774
Coefficient of variation (CV)-0.86986332
Kurtosis0.1015301
Mean-0.01122737
Median Absolute Deviation (MAD)0.0060911016
Skewness-0.28577377
Sum-16.39196
Variance9.5380174 × 10-5
MonotonicityNot monotonic
2024-04-27T17:15:17.356995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.007433783729 7
 
0.5%
-0.005660141353 7
 
0.5%
-0.008355100639 5
 
0.3%
0.001290955348 4
 
0.3%
-0.004459209275 4
 
0.3%
-0.007705844007 4
 
0.3%
-0.02605067194 3
 
0.2%
-0.03193217888 3
 
0.2%
-0.00421575876 3
 
0.2%
-0.01407897566 3
 
0.2%
Other values (1392) 1417
97.1%
ValueCountFrequency (%)
-0.04465026036 1
0.1%
-0.04098113254 1
0.1%
-0.03934597597 1
0.1%
-0.03866941854 1
0.1%
-0.03815709427 1
0.1%
-0.03804508597 1
0.1%
-0.0376848802 1
0.1%
-0.03765797988 1
0.1%
-0.03740137443 1
0.1%
-0.03715289757 1
0.1%
ValueCountFrequency (%)
0.0217233561 1
0.1%
0.01943460666 1
0.1%
0.0171684213 1
0.1%
0.01504915953 1
0.1%
0.01499787066 1
0.1%
0.01444369368 1
0.1%
0.0136294961 1
0.1%
0.01322430931 1
0.1%
0.01267743576 1
0.1%
0.0125693446 1
0.1%

SalePrice
Real number (ℝ)

HIGH CORRELATION 

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:17.439795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2024-04-27T17:15:17.522555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

HouseCategory
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8253425
Minimum0
Maximum13
Zeros36
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-04-27T17:15:17.588562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8
Q39
95-th percentile11.05
Maximum13
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7559605
Coefficient of variation (CV)0.64476218
Kurtosis-1.5438148
Mean5.8253425
Median Absolute Deviation (MAD)2
Skewness-0.14407291
Sum8505
Variance14.107239
MonotonicityNot monotonic
2024-04-27T17:15:17.652309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9 286
19.6%
1 273
18.7%
8 252
17.3%
2 185
12.7%
10 121
8.3%
3 108
 
7.4%
7 85
 
5.8%
12 72
 
4.9%
0 36
 
2.5%
5 15
 
1.0%
Other values (4) 27
 
1.8%
ValueCountFrequency (%)
0 36
 
2.5%
1 273
18.7%
2 185
12.7%
3 108
 
7.4%
4 10
 
0.7%
5 15
 
1.0%
6 5
 
0.3%
7 85
 
5.8%
8 252
17.3%
9 286
19.6%
ValueCountFrequency (%)
13 1
 
0.1%
12 72
 
4.9%
11 11
 
0.8%
10 121
8.3%
9 286
19.6%
8 252
17.3%
7 85
 
5.8%
6 5
 
0.3%
5 15
 
1.0%
4 10
 
0.7%

Interactions

2024-04-27T17:15:12.272526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:51.838728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:52.986658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:54.090079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.481140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.587504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.723810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:58.895387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:00.015024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:01.098020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:02.214411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.388011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.749717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.809181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:06.875383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.927782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.998280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.064746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:11.166930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.328269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:51.897192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:53.049489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:54.146972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.538115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.643548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.785496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:58.953392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:00.069153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:01.153018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:02.271349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.444032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.804664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.864486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:06.929230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.980906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:09.056404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.124574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:11.226098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.385158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:51.953221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:53.107504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:54.201849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.593833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.702349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.846480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:59.007086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:00.121926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:01.215748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:02.332180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.502784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.857713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.918152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:06.982336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.035607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:09.108130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.181580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:11.284100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.440926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:52.013261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:53.167459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:54.258058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.652838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.757202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.910043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:59.069078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:00.183127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:01.270633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:02.393703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.557930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.911933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.977028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.037114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.089726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:09.161251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.240245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:11.343008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.497245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:52.068673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:53.228086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:54.326936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-27T17:15:06.588094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.642298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.695693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:09.777897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.877454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:11.981273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:13.404271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:52.725682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:53.855820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.040593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.346555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.462990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:58.639577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:59.770935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:00.862164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:01.956543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.132645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.245281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.568082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:06.642631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.699204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.750226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:09.834593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.933304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.037019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:13.457163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:52.788322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:53.913691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.099987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.403609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.526523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:58.704422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:59.842383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:00.917703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:02.019067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.192438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.557664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.619806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:06.695363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.753081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.803206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:09.890163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.990435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.094236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:13.516263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:52.852323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:53.969829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.164005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.461609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.589100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:58.766454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:59.901355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:00.977847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:02.084034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.261422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.635029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.679812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:06.760742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.814003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.862089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:09.947006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:11.048266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.153773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:13.577127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:52.919873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:54.033344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:55.421352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:56.527396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:57.655103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:58.835058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:14:59.957381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:01.037569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:02.152578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:03.330090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:04.692719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:05.744349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:06.819392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:07.871006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:08.943651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:10.007888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:11.108885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T17:15:12.214533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-27T17:15:17.718469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1stFlrSFBedroomAbvGrBsmtFinSF1BsmtUnfSFFireplacesFullBathGarageAreaGarageCarsGarageYrBltGrLivAreaHouseCategoryKitchenAbvGrLotAreaLotFrontageMSSubClassMoSoldOpenPorchSFOverallCondOverallQualSalePriceTotRmsAbvGrdTotalBsmtSFYrSoldada_embedding
1stFlrSF1.0000.1410.3230.2240.3410.2580.4900.2380.2500.4940.2630.0410.4440.435-0.2780.0540.235-0.1670.4090.5750.3620.8290.008-0.177
BedroomAbvGr0.1411.000-0.0840.1580.1070.4480.1120.134-0.0440.543-0.4620.2330.3380.3330.0690.0510.100-0.0040.1220.2350.6680.0590.021-0.025
BsmtFinSF10.323-0.0841.000-0.5740.2980.1580.2440.1760.1060.0570.1470.0000.1720.175-0.108-0.0160.081-0.0110.1330.302-0.0500.4100.0000.062
BsmtUnfSF0.2240.158-0.5741.0000.0870.1870.1090.1790.1740.253-0.0760.0620.0780.087-0.1180.0370.156-0.1280.2730.1850.2610.3290.043-0.238
Fireplaces0.3410.1070.2980.0871.0000.1800.2650.2020.1180.481-0.1400.0860.3500.2740.0190.0440.219-0.0450.4210.5190.3470.3260.030-0.138
FullBath0.2580.4480.1580.1870.1801.0000.4440.3290.5330.658-0.3770.1130.2350.2240.1950.0670.370-0.2620.5760.6360.5590.3280.000-0.158
GarageArea0.4900.1120.2440.1090.2650.4441.0000.7590.6040.468-0.1450.0920.3670.379-0.0470.0330.338-0.2010.5420.6490.3310.4870.000-0.221
GarageCars0.2380.1340.1760.1790.2020.3290.7591.0000.6320.505-0.1950.1230.3400.3420.0240.0400.343-0.2550.6090.6910.3860.4560.000-0.207
GarageYrBlt0.250-0.0440.1060.1740.1180.5330.6040.6321.0000.294-0.1680.1820.0690.0860.0730.0130.386-0.3550.6100.6010.2030.3550.000-0.146
GrLivArea0.4940.5430.0570.2530.4810.6580.4680.5050.2941.000-0.5400.0000.4490.3910.2040.0810.398-0.1540.6030.7310.8280.3710.042-0.191
HouseCategory0.263-0.4620.147-0.076-0.140-0.377-0.145-0.195-0.168-0.5401.0000.100-0.148-0.108-0.313-0.025-0.2040.085-0.294-0.269-0.5200.2160.0560.063
KitchenAbvGr0.0410.2330.0000.0620.0860.1130.0920.1230.1820.0000.1001.000-0.023-0.0010.2770.028-0.112-0.100-0.192-0.1650.222-0.0250.0000.030
LotArea0.4440.3380.1720.0780.3500.2350.3670.3400.0690.449-0.148-0.0231.0000.689-0.2700.0060.177-0.0470.2330.4560.4060.3660.000-0.114
LotFrontage0.4350.3330.1750.0870.2740.2240.3790.3420.0860.391-0.108-0.0010.6891.000-0.3010.0170.172-0.0460.2210.4090.3710.3720.014-0.168
MSSubClass-0.2780.069-0.108-0.1180.0190.195-0.0470.0240.0730.204-0.3130.277-0.270-0.3011.0000.0180.032-0.0720.1080.0070.166-0.3190.0000.062
MoSold0.0540.051-0.0160.0370.0440.0670.0330.0400.0130.081-0.0250.0280.0060.0170.0181.0000.066-0.0070.0610.0690.0400.0300.155-0.060
OpenPorchSF0.2350.1000.0810.1560.2190.3700.3380.3430.3860.398-0.204-0.1120.1770.1720.0320.0661.000-0.1330.4350.4780.2850.2700.000-0.105
OverallCond-0.167-0.004-0.011-0.128-0.045-0.262-0.201-0.255-0.355-0.1540.085-0.100-0.047-0.046-0.072-0.007-0.1331.000-0.178-0.129-0.105-0.2170.0500.002
OverallQual0.4090.1220.1330.2730.4210.5760.5420.6090.6100.603-0.294-0.1920.2330.2210.1080.0610.435-0.1781.0000.8100.4280.4600.000-0.256
SalePrice0.5750.2350.3020.1850.5190.6360.6490.6910.6010.731-0.269-0.1650.4560.4090.0070.0690.478-0.1290.8101.0000.5330.6030.000-0.245
TotRmsAbvGrd0.3620.668-0.0500.2610.3470.5590.3310.3860.2030.828-0.5200.2220.4060.3710.1660.0400.285-0.1050.4280.5331.0000.2340.000-0.129
TotalBsmtSF0.8290.0590.4100.3290.3260.3280.4870.4560.3550.3710.216-0.0250.3660.372-0.3190.0300.270-0.2170.4600.6030.2341.0000.000-0.166
YrSold0.0080.0210.0000.0430.0300.0000.0000.0000.0000.0420.0560.0000.0000.0140.0000.1550.0000.0500.0000.0000.0000.0001.0000.043
ada_embedding-0.177-0.0250.062-0.238-0.138-0.158-0.221-0.207-0.146-0.1910.0630.030-0.114-0.1680.062-0.060-0.1050.002-0.256-0.245-0.129-0.1660.0431.000

Missing values

2024-04-27T17:15:13.687758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-27T17:15:13.857304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MSSubClassLotFrontageLotAreaOverallQualOverallCondBsmtFinSF1BsmtUnfSFTotalBsmtSF1stFlrSFGrLivAreaFullBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaOpenPorchSFMoSoldYrSoldada_embeddingSalePriceHouseCategory
00.2352940.1506850.0334200.6666670.5000.1250890.0642120.1400980.1197800.2592310.6666670.3750.3333330.5000000.0000000.9363640.500.3864600.1115170.0909090.50-0.0074342085001.0
10.0000000.2020550.0387950.5555560.8750.1732810.1215750.2065470.2129420.1748300.6666670.3750.3333330.3333330.3333330.6909090.500.3244010.0000000.3636360.25-0.0046921815007.0
20.2352940.1609590.0465070.6666670.5000.0861090.1857880.1505730.1344650.2735490.6666670.3750.3333330.3333330.3333330.9181820.500.4287730.0767820.7272730.50-0.0093512235001.0
30.2941180.1335620.0385610.6666670.5000.0382710.2311640.1237320.1438730.2605500.3333330.3750.3333330.4166670.3333330.8909090.750.4527500.0639850.0909090.00-0.0256261400002.0
40.2352940.2157530.0605760.7777780.5000.1160520.2097600.1873980.1860950.3511680.6666670.5000.3333330.5833330.3333330.9090910.750.5895630.1535651.0000000.50-0.0079682500001.0
50.1764710.2191780.0598990.4444440.5000.1296950.0273970.1302780.1060120.1936700.3333330.1250.3333330.2500000.0000000.8454550.500.3385050.0548450.8181820.75-0.0227381430001.0
60.0000000.1849320.0410570.7777780.5000.2425580.1357020.2759410.3120700.2562170.6666670.3750.3333330.4166670.3333330.9454550.500.4485190.1042050.6363640.25-0.0060273070008.0
70.2352940.2191780.0424500.6666670.6250.1521970.0924660.1811780.1773750.3308210.6666670.3750.3333330.4166670.6666670.6636360.500.3413260.3729430.9090910.75-0.0197492000000.0
80.1764710.1027400.0225290.6666670.5000.0000000.4075340.1558100.1578710.2712890.6666670.2500.6666670.5000000.6666670.2818180.500.3300420.0000000.2727270.50-0.0115171299002.0
91.0000000.0993150.0286050.4444440.6250.1507800.0599320.1621930.1704910.1399770.3333330.2500.6666670.2500000.6666670.3545450.250.1445700.0073130.0000000.50-0.0307911180002.0
MSSubClassLotFrontageLotAreaOverallQualOverallCondBsmtFinSF1BsmtUnfSFTotalBsmtSF1stFlrSFGrLivAreaFullBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaOpenPorchSFMoSoldYrSoldada_embeddingSalePriceHouseCategory
14500.4117650.1335620.0359910.4444440.5000.0000000.3835620.1466450.1289580.2746800.6666670.5000.6666670.5000000.0000000.3654550.000.0000000.0822670.7272730.75-0.0132511360000.0
14510.0000000.1952050.0372150.7777780.5000.0000000.6733730.2574470.2854520.2343630.6666670.3750.3333330.4166670.3333330.9818180.750.5923840.0658140.3636360.75-0.0257312870908.0
14520.9411760.0479450.0111010.4444440.5000.0969170.0000000.0895250.1693440.1390350.3333330.2500.3333330.2500000.0000000.9545450.500.3702400.0511880.3636360.00-0.00873214500012.0
14530.0000000.2363010.0743980.4444440.5000.0000000.4880140.1865790.1849470.1518460.3333330.3750.3333330.3333330.0000000.6163640.000.0000000.1023770.5454550.00-0.013397845008.0
14540.0000000.1404110.0289790.6666670.5000.0726440.3471750.1998360.2035340.1671060.6666670.2500.3333330.3333330.0000000.9454550.500.2820870.2065810.8181820.75-0.0123871850008.0
14550.2352940.1404110.0309290.5555560.5000.0000000.4079620.1559740.1420380.2473620.6666670.3750.3333330.4166670.3333330.9000000.500.3244010.0731260.6363640.25-0.0027561750001.0
14560.0000000.2191780.0555050.5555560.6250.1399720.2521400.2523730.3990360.3276190.6666670.3750.3333330.4166670.6666670.7090910.500.3526090.0000000.0909091.00-0.0104822100008.0
14570.2941180.1541100.0361870.6666671.0000.0487240.3754280.1885430.1959610.3779200.6666670.5000.3333330.5833330.6666670.3727270.250.1777150.1096890.3636361.00-0.0095612665003.0
14580.0000000.1609590.0393420.4444440.6250.0086820.0000000.1764320.1707210.1401660.3333330.2500.3333330.2500000.0000000.4545450.250.1692520.0000000.2727271.00-0.00085714212510.0
14590.0000000.1849320.0403700.4444440.6250.1470590.0582190.2055650.2115650.1737000.3333330.3750.3333330.3333330.0000000.5909090.250.1946400.1243140.4545450.50-0.0005151475009.0